As the learning algorithm, we choose multi-class confidence-weighted (CW) learning (Crammer et al., 2009) which has been shown to perform well for NLP problems with high dimensional and sparse feature spaces.


Furthermore, speech recognition errors only resulted in a minor performance drop.

first, the length of the spoken responses are typically shorter than written responses.


More recently, Lu (2010), Chen and Yoon (2011) and Chen and Zechner (2011) have measured syntactic competence in speech scoring.

the increased proportion of these tags reflects the use of more complicated tense forms and modal forms as well as more frequent use of relative clauses.


This study presents a novel method that measures English language learners' syntactic competence towards improving automated speech scoring systems.

the more frequent the POS tag, the higher its rank.


We plan to make all our acquired knowledge resources available to the research community soon (Visit http://www.alagin.jp/index-e.html).

we evaluated only PROP caus , since we could not think of any reasonable baseline for this task.


npf req(n 1 , n 2 ) is the co-occurrence frequency of (n 1 , n 2 ) with polarity-identical template pairs (if (n 1 , n 2 ) is PNP) or with polarity-opposite template pairs (if (n 1 , n 2 ) is NNP).

this section shows that our template acquisition method acquired many Excitation templates.


Pattern matching alone may generate inappropriate pairs, so we then assess the legitimacy of each collected slot-filler.

human evaluators were then asked to score each pair of an original sentence and a paraphrased sentence with the following two 5-point scale grades proposed by Callison-Burch (2008): Grammaticality: whether the paraphrased sentence is grammatical (1: horrible, 5: perfect) Meaning: whether the meaning of the original sentence is properly retained by the paraphrased sentence (1: totally different, 5: equivalent) To make results more consistent and reduce the human labor, evaluators were asked to rate at the same time several paraphrases for the same source phrase.


The remaining 350 examples were divided into six chunks of slightly unequal length, with each chunk being judged by one of the six evaluators.

monolingual comparable corpora have also been exploited as sources of paraphrases using alignmentbased methods.


For instance, when the full size of bilingual parallel corpus in Patent was used, we acquired 1.41M pairs of seed paraphrases, P Seed , and 28.7M pairs of novel paraphrases, P Hvst .

this is a benefit from our generalization and instantiation method.


Such corpora have also been created manually through crowdsourcing (Chen and Dolan, 2011).

unlike in the pure similarity-based method, P Hvst is constrained by the paraphrase patterns derived from the set of highquality paraphrases, P Seed , and will therefore gen-erally exclude the kind of semantically similar but non-equivalent pairs that contextual similarity alone tends to extract alongside real paraphrases.


1is used (Du et al., 2010;Max, 2010;Denkowski and Lavie, 2011, etc.).

we assume the phrase-based SMT framework (Koehn et al., 2003).


Table 1 gives the results of a comparison for two document representations and their different settings.

we should point out that in the images the shading between points is not intended to suggest interpolation but is used to highlight the overall pattern.


The advantage of RANK over SFA is disputable as there is not much consistency about when one algorithm outperforms another, except that SFA is better overall for close domains.

preliminary experiments show a big advantage of SO-CAL-dictionaries comparing with other resources.


Analysing differences in the graph structures and assumptions of both models we can say that they are almost identical.

the authors presented a method for automatic construction of a sentiment-sensitive thesaurus where each lexical element (either unigram or bigram) is connected to a list of related lexical elements which most frequently appear in the context expressing the same sentiment.


MRF wm (Row 7) uses the word translation model described in Section 2.1.

the result confirms the conclusion of Riezler et al., demonstrating that context information is crucial for improving retrieval precision by filtering noisy expansions.


Third, the term correlations evolve along with the accumulation of user logs, thus can reflect updated user interests at a specific time.

in addition, as pointed out by Cui et al.


First, a query is represented as a vector of topics.

to instantiate the MRF model, one needs to define a graph structure and a set of potential functions.


It is important to note that our interval-based formulation is more concise in terms of the number of variables and constraints needed in the ILP relative to time expression-based (or timepoint-based) formulations used in previous work (Chambers and Jurafsky, 2008).

to overwrite the classification probabilities using event coreference knowledge, we propose two approaches as follows: MaxScore: We define the probability between any mention e ∈ E i and an interval I as follows: whereP (e , I) is the classifier (C E−t ) probability for associating event mention e to the time interval.


, I m } denote the set of time intervals extracted from an article, and let E = {e 1 , e 2 , .

e 5 and e 3 both happen in the interval I 2 but they form an overlap relation.


Implicit intervals are also added according to Sec.

the inference models in their work were not a joint model involving multiple local classifiers but only one local classifier was involved in their objective functions.


Using punctuation to divide input sentences into two groups, DBM-2 scored higher: 40.9, up from 39.0% accuracy (see Table 2).

other orthogonal dependency grammar induction techniques -including ones based on universal rules (Naseem et al., 2010) -may also benefit in combination with DBMs.


Table 5: A distance matrix for all pairs of probability distributions over POS-tags shown in Table 4 and the uniform distribution; the BC-(or Hellinger) distance (Bhattacharyya, 1943;Nikulin, 2002) between discrete distributions p and q (over x ∈ X ) ranges from zero (iff p = q) to one (iff p • q = 0, i.e., when they do not overlap at all).

(ii) Punctuation at sentence boundaries further helps distinguish full sentences from fragments like headlines and titles, allowing us to model grammatical differences between complete and incomplete sentences.


2 Thus, non-adjacent stopping decisions will be made independently of a head word's identity.

we build on three intuitions that are explicit in phrase-structure grammars but only implicit in standard dependency formulations: (i) Distributions of words that occur at sentence boundaries-such as English determiners-resemble constituent edges.


Our models follow a standard generative story for head-outward automata (Alshawi, 1996a), restricted to the split-head case (see below), 1 over lexical word classes {cw}: first, a sentence root cr is chosen, with probability PATTACH(cr | ⋄; L); ⋄ is a special start symbol that, by convention (Klein and Manning, 2004;Eisner, 1996), produces exactly one child, to its left.

we presuppose that all sentences are assigned to one of two classes: complete and incomplete (comp ∈ {T, F}, for now taken as exogenous).


For the maxent models, in contrast, the majority of the topics were associated with a higher number of n-grams (Table 9).

the latter approach is actually closer to the work of Hardisty et al.


As we are using the same data set as per the previous approach, we perform 5-fold cross validation as well.

we would like to acknowledge the support of ARC Linkage Grant LP0776267.


Note that, in our later experiments, we fix T to moderate values (T = 5, 8) for ease of analysis and display.

the supplement contains illustrations of one of the resulting graphs.


However, the scale and complexity of such collections frequently make it difficult to quickly grasp the important details and the relationships between them.

constructing the graph only requires an additional 160 seconds though.


We showed them timelines into which two additional "interloper" articles selected at random had been inserted, and asked them to remove the two articles that they thought should be removed to "improve the flow of the timeline".

in order to convert the weight function defined in Equation (1) to the appropriate multiplicative form, we use a simple log-linear model, setting q(y i ) = exp(λw(y i )), where λ is a hyperparameter that effectively governs the balance between quality and diversity by adjusting the dynamic range of the quality function.


Positive examples for the classifier are those from the union of candidates that are also in the reference set R sure , while negative examples are the remaining ones from the union.

their low availability makes searching for less parallel corpora a necessity.


The semantic parser is trained using sentences from a web corpus, which contains many out-of-domain words.

the main input to our system is a propositional knowledge base K = (E, R, C, ∆), containing entities E, categories C, relations R and relation instances ∆.


It segments a word sequence {w 1 , w 2 , w 3 } into a phrase sequence {w 1 , w 2 w 3 } after performing composition (Mohri, 2009) with the target L w language model (see Figure 3(b1 & b2)) 3 .

prior to phrasal reordering, the segmentation model p (w K 1 |w J 1 ) implemented by a segmentation WFST S w is applied to segment a word sequence w J 1 in the L w language model into a phrase sequence {w 1 ,w 2 , ...,w K }.


Given sufficient parallel training data, the contextdependent phrase-to-phrase transduction model can be estimated using the GIATI method (Casacuberta and Vidal, 2004).

a reordering model with reordering constraints, such as ITG constraints (Wu, 1997), IBM constraints (Berger et al., 1996), and local constraints (Kumar and Byrne, 2005) can account for the syntactic differences.


These results suggest that our features are more useful for resolving difficult pronouns than those commonly used for coreference resolution.

to acquire this kind of world knowledge, we learn patterns of word usage from the Web by issuing search queries.


This work was supported in part by NSF Grants IIS-0812261 and IIS-1147644.

after obtaining the query counts, we generate four binary features, FN1, FN2, FN3, FN4, whose values 6 We heuristically map grammatical roles to semantic roles.


We employ machine learning to combine the features derived from different knowledge sources.

note that many traditional linguistic constraints on coreference are no longer useful for resolving difficult pronouns.


In order to compare the corpora we categorise the quotes into the categories defined by Elson and McKeown (2010), as shown in Table 1.

moving to a higher-order model is problematic as the number of transition probabilities that need to be calculated increases exponentially with the order of the model.


This work was supported by the U.S. Department of Defense through the U.S. Army Research Office (grant number W911NF-10-1-0533) and via a National Defense Science and Engineering Graduate Fellowship for the first author.

this basic approach for emission probabilities produces bad unknown-word probabilities.


From the results, we can also find that there is no clear winner between GRAPE and WS.

in this paper, we explore the feature space in the web person name disambiguation task and propose a topic-based model which exploits corpus level person specific evidences to handle the data sparsity challenges, especially the case that limited evidences can be collected from the local context.


We use the 50 classes automatically generated by Och (1999)'s method that are calculated during alignment in standard SMT systems.

this section explains how to calculate oracle reorderings, and assign each F a loss and an accuracy according to how well it reproduces the oracle.


that spanned constituent boundaries (as long as the phrase frequency was high).

for each non-terminal node spanning f with its left child spanning f 1 and its right child spanning f 2 , if the non-terminal symbol is str, the reordered strings will be concatenated in order as f = f 1 f 2 , and if the non-terminal symbol is inv, the reordered strings will be concatenated in inverted order as f = f 2 f 1 .


Finding the appropriate word ordering in the target language is one of the most difficult problems for statistical machine translation (SMT), particularly for language pairs with widely divergent syntax.

, a J , where each a j is a set of the indices of the words in E to which f j is aligned.


Next, we find the model parseḊ i , which is the parse in the forest D i that maximizes the sum of the model score and the loss It should be noted that here we are considering not only the model score, but also the derivation's loss.

there is a large amount of previous research that handles the problem of reordering through the use of improved reordering models for phrase-based SMT , hierarchical phrase-based translation (Chiang, 2007), syntax-based translation (Yamada and Knight, 2001), or preordering (Xia and McCord, 2004).


Although IDG is simple and efficient, and has been shown to be effective in phrase-based SMT, it is problematic in SSMT, as illustrated by the example in section 1.

any improvement from the default parser, which is trained on WSJ, is expected to be smaller in the NIST case.


One is to apply it to the entire data set, with no separate training phase.

for our machine translation experiments, we used the string-to-tree syntactic pipeline included in the current version of Moses (Koehn et al., 2007).


4 In principle, our transformation inventory consists of all possible assignments of syntactic categories to the arguments of each of the transformation types (subject to the triggering environment constraints).

we are concerned more generally with the goal of creating trees that are more compatible with a wide range of syntactically-informed translation systems, particularly those that extract translation rules based on syntactic constituents.


Examples include whether one typically goes to school first or starts their first job, how many jobs people typically have at one time, or if it is possible to marry someone who hasn't been born yet.

the JCC model (76.1% on development with 100% training entities) is consistently the best non-oracle system.


Queries are named entities (people or organizations) with their gold relations but no temporal bounds.

it is difficult to obtain these bounds without manual annotation.


While simple joint inference methods such as random-restart hillclimbing and Gibbs sampling worked well in our case, more complex inference methods may be required with more elaborate constraints.

both "1981" and "September 1981" would have the START metarelation but "September 2nd, 1981" would be assigned UNRELATED.


There exists, however, a great challenge to compile consistently relations extracted from text by these methods, because they assume a simplifying assumption that relations are time-invariant.

a relation is constant if, for most values of arg1, the value of arg2 is independent of time ( Table 1).


The feature is fired if the number of times that an entity in arg2 is followed by one of the four keywords exceeds threshold θ 3 .

arg1 's president is arg2 is an example of non-constant relations.


If the production has not been observed frequently enough, it uses its other variations to perform further smoothing.

every new subset of features appears to improve the accuracy.


Table 4: Segmentation results of different models.

we compute the MLE estimates for a production and its other variations, and use those as features with/without binarizing the values.


With N-gram features (S 3 ), the gain is even higher; 6% in relations and 3.5% in nuclearity, demonstrating the utility of the N-gram features.


Our approach for sentential paraphrase extraction is related to the one introduced by Barzilay and Lee (2003), who also employ multiple sequence alignment (MSA).

1 shows the evaluation results on our gold-standard.


We employ a statistical linearizer that is trained on a corpus of pairs consisting of unordered dependency trees and their corresponding sentences.

the bottom-up linearization algorithm can take into account features drawn from the already ordered subtrees while the top-down algorithm can employ as context only the unordered nodes.


When all nodes have been visited, the best tree in the agenda is returned.

the range of sentences affected by the lifting is clearly largest for the V2languages.


Section 5.4 explores morphologic and orthographic features as additional sources of information and its results improve the state-of-the-art in the field of unsupervised syntactic category acquisition.

section 2 describes a number other approaches that show similar improvements.


For example, one model might produce topics that cover ideas such as government affairs, sports, and movies.


In practice, we are not searching in the space of possible translations, but in the space of possible derivations, which are sequences of phrase translations p 1 (s 1 , t 1 ), ..., p n (s n , t n ) that can be applied to s to generate an output t with the score given by P (t) n i=1 P (s i , t i ).

in our metric, we do not have any means for detecting spurious phrase pairs, in fact, spurious phrase pairs are probably kept in the phrase table, since each distinct spurious phrase pair is only extracted once, and thus, they have very few derivations in its support set.


Thus, phrase pairs for number sequences can be removed, since those numbers can be translated one by one.

in another words, the model's predictions even, without this phrase pair will remain the same.


While the concept of using relative entropy for pruning is not new and frequently used in backoff language models, there are no such models for machine translation.

given a s, t pair one of three situations may occur.


We see that 96% of all phrases are compositional (3 970 million out of 4 137 million phrases).

we found that this does not affect transla- tion quality significantly 4 .


A phrase-based system selects among different segmentations of the source language sentence into phrases.

we found that many existing methods employ ad-hoc heuristics without theoretical foundation.


One of the findings in our experimentation is that paraphrasing helps boosting model accuracy, and the idea of using paraphrases in MT evaluation was first proposed by Zhou et al.

the goal of our experiments is to test both the accuracy and robustness of the proposed new models.


TERR clearly suffered from not being able to model word swapping in this case.

their model is largely empirical and not in a probabilistic learning setting.


3 Note that the bootstrap procedure given only approximates the true significance level, with multiple sources of approximation error.

these results indicate that the similarity of the systems being compared is an important factor.


The bootstrap in particular is easy to run and makes relatively few assumptions about the task or evaluation metric.

it should be considered a good practice to include statistical significance testing results with empirical evaluations.


from the same distribution that generates new data, then significance levels are remarkably well-calibrated.

we train the unsupervised word aligners using the 1.1M sentence pair Hansard training corpus, resampling 20 training sets of the same size.


Together, this yields a total of 75 system outputs on the CoNLL 2007 Chinese test set, 25 systems for each base model type.

what does this relationship look like and how reliable is it?


For example, although both word "劝解" (console) and "分解" (decompose) are constructed form verb "解", their verb structure (verb + BV) does not appear in the training set.

for example, although both word "劝解" (console) and "分解" (decompose) are constructed form verb "解", their verb structure (verb + BV) does not appear in the training set.


Finally once we have trained a PRA model for a given relation, say profession, we would like to infer the professions for all the 1.7 million persons whose professions are not known to Freebase (and possibly predict changes to the profession information of the 0.3 million people whose professions were given).

in each of the parsed documents, we use POS tags and dependency edges to identify potential referring noun phrases (NPs).


As in our work, they use a logistic regression model with path features.

as we are processing Web text data (see following section for more detail), the number of mentions of a concept follows a somewhat heavy-tailed distribution: there are a small number of very popular concepts (head) and a large number of not so popular concepts (tail).


Later systems for extracting arbitrary relations from text mostly use shallow surface text patterns (Etzioni et al., 2004;Agichtein and Gravano, 2000;Ravichandran and Hovy, 2002).

these features are active only for specific subsets of queries.


We believe that the real bottleneck of parsing applications is parsing time and not training time.

the final output of forest ranking is the 1-best subtree headed by the goal node S 1 (v goal ).


Table 1 shows the standard performance metrics, measured on section 23 of the WSJ, using all sentences.

clegg and Shepherd (2005) evaluated parsers qualitatively on node types and rule productions.


Some of the frequent error types that we identify are widely recognised as challenging, such as prepositional phrase (PP) attachment.

single word phrase errors are second most important by number of groups (in Table 3), but seventh by total number of node errors (in Table 2).


The QUERY procedure for CM-CU is identical to Count-Min.

we describe a system, FLAG, for generating a nearest neighbor graph from a large corpus.


We use PMI and LLR to compute the association score (AS) between the term and each of the context to generate the context vector.

this results in an average stream size of 194 million word pair tokens and 33.5 million word pair types per chunk.


Such vast corpora have led to leaps in the performance of many language-based tasks: the concept is that simple models trained on big data can outperform more complex models with fewer examples.

from GW and 13.20B tokens and 0.8B types from GWB50.


We use recall to measure the number of top-K sorted word pairs that are found in both the rankings.

we focus on solving this central problem in the context of NLP applications.


The citation matching problem is an instance of entity resolution, in which observed mentions need to be partitioned such that mentions in a set refer to the same underlying entity.

in this paper we propose an alternative MCMC sampling scheme in which transition probabilities are approximated by sampling from the set of relevant factors.


Based on the ideas presented in the paper, we will consider additional sampling strategies.

evaluation of such a proposal function requires scoring a number of factors linear in the size of the entities, which, for large datasets, can be a significant bottleneck.


Now, the overall goal of inference is to find the highest scoring structure.

our implementation uses the following weaker version of this optimization: while caching ILPs, we do not add an instance to the cache if it already satisfies the theorem.


We can see that while increasing the n-gram model size, our method (ARS) exhibits a linear increase in decoding time, in contrast to the exponential factor exhibited when running the Viterbi algorithm over the full WFSA (F).

finally in both figs 4c and 4d, we show the average number of iterations taken to sample from the entire test-test, and the average number of states in our variable-order HMMs, with AR-100=60%.


Accept x with probability p(x)/q(x), otherwise reject x; To obtain multiple samples, the algorithm is repeated several times.

here, a refinement refers to the increase of the order of some of the max-backoff weights in the current proposal (thus most refinements consist of n-grams with heterogeneous max-backoff orders, not only those shown in equations (3)-(6)).


However, no attempt is made to organize these patterns into synonymous patterns, let alone into a taxonomy.

we introduce the special POStag [word], which stands for any word of any POS class.


The support set of a pattern is the set of pairs of entities that appear in the place of the entity placeholders in all strings in the corpus that match the pattern.

if a support set S has only few elements due to sparsity, it may become a subset of another support set B, even if the two patterns are semantically different.


Mean field (Saul and Jordan, 1996) is another approximate inference technique that allows for structured approximations (Xing et al., 2003;Burkett et al., 2010), but here the natural version of mean field for our model would still be intractable.

(c) Fitting the approximation with expectation propagation, as described in Section 5.3.


θ m will be particularly motivated to correct the latter, because they are less like the gold tree.

many high-performance PCFG parsers take an initially simple base grammar over treebank labels like NP and enrich it with deeper syntactic features to improve accuracy.


Nevertheless, it is a shame to sacrifice expert annotation just to get latent annotations.

we are not aware of a prior discriminative lexicalized constituency parser, and it is quite different from the generative models of Collins (1997) The second is similar to the unlexicalized model of Klein and Manning (2003).


Columns are created one for each entity appearing in the text.

each sentence in the article has a communicative goal and the sequence of goals helps the author achieve the discourse purpose.


As alluded to in the introduction, the first few words of a sentence fragment are typically scored using lower-order entries from an N -gram language model.

change is the cost of adding an additional value to store lowerorder probabilities.


While our decoding algorithm does not impose any formal restrictions on the number or type of dependencies that can be handled, there will be practical limits beyond which decoding becomes unacceptably slow or the scoring code becomes very difficult to maintain.

if optimal results are sought after, it pays off to invest some effort into a good starting point.


We compare to several baselines and ablations of the MV-RNN model.

can it learn some of the propositional logic operators such as and, or, not in terms of vectors and matrices from a few examples.


Unfortunately, current state-of-the-art NLP tools do not work well on transcribed text.

6 Experiments and Results We use the official ACE 2004 English training data (NIST, 2004).


We note that both IC R i and IC E i can have the values +1 and -1 active at the same time if intermediate sieve classifiers generated conflicting predictions.

after this pre-processing step, we were left with 275 out of the original 336 training documents, and 42 out of the 107 testing documents.


Best-First constraint: Here we assume that coref(j,i) returns true if candidate mentions j and i are coreferential and false otherwise.

for generation of training instances, we follow the method of Bengtson and Roth (2008).


In the future we will try to design more global constraints and explore deeper relations between training instances generation and mention clustering.

a mention clustering step is needed to resolve conflicts and generate the final mention clusters.


Similarly ETLM qa also performs better than ETLM qa .

it provides for context aware expansions of the query by exploiting entity annotations on both, the document and the query side.


In the test, we search each candidate concept in the Google search engine and remove a candidate if it appears <4 times within the top 10 Google snippets.

another common function of taxonomies, browsing, has received little attention in the NLP community.


While Jeopardy also uses signaling devices, these are only usable after a question is completed (interrupting Jeopardy's questions would make for bad television).

teams interrupt the question at any point by "buzzing in"; if the answer is correct, the team gets points and the next question is read.


Logistic Regression (LR) A single logistic regression model run over all the training data, ignoring domain labels.

we select the four domains used in most studies: books, dvd, electronics and kitchen appliances.


In this light, these methods are a complex form of instance bagging, and their development could be justified from this perspective.

to obtain a single classifier we use two classification algorithms: SVMs and logistic regression.


The key question of this paper is: when do domains matter?


So we need to set σ to a value that is large enough to induce unambiguity.

the softmax-EM algorithm can be implemented with a simple and computationally efficient extension to standard EM.


One implication of this difference is that if our approach is applied to semi-supervised learning, the regularization term would be applied to labeled sentences as well (by ignoring the labels) because the target grammar shall be unambiguous on all the training sentences.

the goal of unambiguity regularization is to bias learning in favor of unambiguous grammars, which is achieved by setting the exponent in the Estep (i.e., 1 1−σ in Eq.1) to a value larger than 1, so that the distribution of the hidden variables becomes less uniform (i.e., parses become less ambiguous); in our annealing approach, the exponent is initialized to a very large value (positive infinity in our experiment) to push the learner away from the ambiguous initial grammar, and then gradually decreased to 1 to avoid inducing excessive unambiguity in the learned grammar.


Others extend the token-level approach to jointly identify opinion holders (Choi et al., 2006), and to determine the polarity and inten-sity of the opinion expressions (Choi and Cardie, 2010).

more specifically, we construct segment units from the parse tree of each sentence 1 , and then build up possible segment candidates based on those units.


These features have been shown useful for the task of NE recognition (Sarawagi and Cohen, 2004;Okanohara et al., 2006).

segment-CRF treats segment units obtained from the parser as word tokens.


Previous work has shown that semi-CRFs outperform CRFs on named entity recognition (NER) tasks (Sarawagi and Cohen, 2004;Okanohara et al., 2006).

for opinion expressions that do not match any segment candidate, we break them down into smaller segments using a greedy matching process.


Semi-CRFs (Sarawagi and Cohen, 2004) are general CRFs that relax the Markovian assumptions to allow sequence labeling at the segment level.

arbitrarily long expressions can be modeled and their boundaries can be influenced by probable syntactic structure.


We extend the original semi-CRF model (Sarawagi and Cohen, 2004) to allow the modeling of arbitrarily long expressions while accounting for their likely syntactic structure when modeling segment boundaries.

we propose a semi-CRF-based approach to the task that can perform sequence labeling at the segment level.


Due to the limitation of space, we only show the F-measure of Ours on COAE2008 dataset2 and Large when varying  in Figure 6.

the candidates with higher confidence scores than the threshold will be extracted as the opinion targets.


To consider the candidate importance scores, we introduce a reallocate condition: combining the candidate opinion relevance with the candidate importance at each step.

1993) to perform monolingual word alignment, which has been widely used in many tasks, such as collocation extraction , question retrieval (Zhou et al., 2011) and so on.


The experimental results on three real world datasets in different sizes and languages show that our approach is more effective and robust than state-of-art methods.

most pervious methods iteratively extracted opinion targets depending upon the associations between opinion words and opinion targets (Qiu et al., 2011;Zhang et al., 2010).


To visualize the top feature weights learned by the model, we have to map the four weight vectors learned by the model back to the underlying twodimensional sentiment/price space.

mENUDESC: n-grams in menu item descriptions, as in mENUNAmES.


This is analogous to the standard E-step in EM.

it is clear that choosing all languages is not a good idea, since they are not all expected to have distributional properties similar to the target language.


To relax this restriction, we will allow A(c|f ) ∈ [0, 1] and encourage A to correspond to a mapping by adding an entropy regularization term: This term receives its minimal value when the conditional probability of the universal tags given a language-specific tag is 1 for one universal tag and zero for the others.

we measure mapping quality based on the target language parsing accuracy.


In this setup, finding the mapping between various POS annotation schemes was not essential; instead, the transfer algorithm could induce it directly from the parallel data (Hwa et al., 2005;Xi and Hwa, 2005;Burkett and Klein, 2008).

the goal of our work is to induce the mapping for a new language, utilizing existing manuallyconstructed mappings as training data.


For each S i as the target, we first train a parser for each language in S \ {S i } as if it was the source, using the map of , and choose S * i ∈ S \ {S i } which gives the highest parsing accuracy on S i .

it will have a value of zero when the bigram distribution induced by A has the property specified in S pre .


The experimental results on these two datasets are shown in Table 9.

where "fan" means "adore" and is used as a verb.


All the above approaches to multilingual or crosslingual WSD rely on bilingual corpora, including those which exploit existing multilingual WordNetlike resources (Ide et al., 2002), or use automatically induced multilingual co-occurrence graphs (Silberer and Ponzetto, 2010).

to overcome this limitation, in this work we make use of BabelNet , a very large multilingual lexical knowledge base.


The predominant domain baseline, available only in the coarse-grained setting, lags behind Domain PPR by more than 3 points in precision and 2 in recall.

this is a perfectly acceptable phenomenon as any of the two senses, i.e., glosses, would be equally valid when disambiguating a domain text dealing with ecosystem restoration.


(2009) learns meaning representations by simultaneously considering documents and features.

for example, suppose a document d j consists of the sentence Mix in the apple, celery, raisins, and apple juice.


We aim for an efficient solution that makes use of the individual solvers at the sentence-level.

for example, in dependency parsing the resulting structure must be a tree.


From Figure 2 we see that besides word segmentation, output of the unified parser also includes partof-speech tags.

to also output part-of-speech tags and dependency labels, the transitions above can be augmented accordingly.


In such a dynamic setting, burst detection algorithms should effectively collect evidence, efficiently adjust prediction models and respond to the users as social media activities evolve.

to overcome this, baseline method StateMachine (g 1 + L = 2) requires larger ρ and γ 1 , which may discard relatively small meaningful bursts; while our proposed single stream method (g 2 + L = 3, 4) tends to identify steady and consecutive bursts through the help of longer context window and context sensitive smoothness function g 2 , it is more suitable to be applied to social media for burst detection.


For su-pervised approaches, major feature sources are the text the user has written and also her profile which may list the name, interests, friends, etc.

we consider the task of user gender prediction on YouTube and contrast two information sources: (1) the comments written by the user and (2) her social neighborhood as defined by the bipartite user-video graph.


Each time, we propose candidate completions at the wordlevel when the user is about to start a new word, or has partially entered the first few letters; once this word is successfully completed, we move on to the next one.

in general, our problem here is a lack of clear word-to-word or phrase-to-phrase mapping in a stimulus-response pair, at least not what one would expect in clean parallel data.


The phrase "credit card", in this case, is incorrectly merged into one word "creditcard" by the user.

since the number keys on keyboard are away from the letter keys, they are more likely to be intentionally typed in if found in user queries.


The method resembles the Expect-Maximization (EM) training method as it updates the model by iteratively recomputing the latent variable.

another heuristic is that words with numbers in it, despite usually not included in any vocabulary, should be treated carefully as they tend to be correct words.


Formally, let us denote q = q 1 , ..., q n and c = c 1 , ..., c m as structured objects from the space of V * , where V is our vocabulary of words and V * is all possible phrases formed by words in V. Both q and c have an intrinsic sequential structure.

it is unknown whether other features can work with the search algorithm and how we can develop new features to ensure it.


This allows us to adapt the model to make it work directly with the search algorithm we use for finding the best correction of the query.

sMT usually involves more complex alignments, while in query spelling correction search is the more challenging part.


The MUC measure is the oldest and most widely used.

8 Comparison with CoNLL-2011 Table 27 shows the performance of the systems on CoNLL-2011 test subset which included only the English portion of OntoNotes v4.0.


Bare plurals, as in (12) and (13), are always considered generic.

a hybrid approach seems to be a central theme of many high scoring systems.


However, maintaining format consistency with earlier CoNLL tasks was deemed convenient for sites that already had tools configured to deal with that format.

one thing that needs to be dealt with in conversational data is the presence of disfluencies (restarts, etc.).


It is almost always the last name of the first author of the system papers, or the first name in case of conflicting last names (xinxin).

we evaluated systems only on the identity coreference task, which links all categories of entities and events together into equivalent classes.


These results are not surprising since to identify non-singleton mentions greatly reduces the final task complexity.

furthermore, this feature induction technique extends the structure perceptron framework by providing an efficient general method to build strong nonlinear classifiers.


For Chinese and English, these performances are virtually identical to the performances on the development sets.

for Chinese and English, these performances are virtually identical to the performances on the development sets.


Note that the two threshold values −0.1 and 1.1 render a probability threshold useless.

we repeat this process until all conditions are removed, and identify the subset of the conditions that yields the best score on the development set.


We thank the two anonymous reviewers for their comments on the paper.

the motivation behind having the rule relaxation parameters should be clear: they allow us to optimize the hand-crafted rules using machine learning.


As shown by the Stanford coreference resolver (Lee et al., 2011), the winner of last year's shared task, many coreference relations in OntoNotes can be identified using a fairly small set of simple hand-crafted rules.

in addition, to better understand the role played by the two sets of system parameters, we performed ab-lation experiments, showing for each language-track combination the results obtained without tuning (1) the rule relaxation parameters (− λ i 's); (2) the probability thresholds (− t j 's); and (3) any of these parameters (− λ i 's & t j ).


We estimate the mention coreference probability of m k from the training data.


We built a classifier  to discard as many of these pleonastic it as possible from the mention list.

when the pair of mentions corresponds to the same speaker, often in combination with the fact that both mentions are pronouns.


Thus we could imagine that projecting the results from English into Chinese and Arabic should still beats the baseline system using monolingual resolution method.

modeling multilingual unrestricted coreference in the OntoNotes data is the shared task for CoNLL-2012.


Suppose there are three mentions A, B, C where the probability between A and B, A and C is larger than , but B and C is small.


Rich and meaningful features are important for coreference resolution.

choosing an appropriate decoding algorithm is necessary.


The set of features that we use, listed in Table 5, is an extension of the set by Rahman and Ng (2009).

we would also like to thank Kiran Kumar for his help with tuning the system.


Following these works, we include a k nearest neighbor classifier for singleton mentions in UBIU with 19 commonly-used features described below.

singleton mentions that are added in this step can be filtered out in later stages.


mentions that are coreferent in the key set but wrongly classified in the answer set are removed, leading to a decrease in recall.


Table 2: The improvement of Illinois-Coref.

in the literature (Bengtson and Roth, 2008;Rahman and Ng, 2011;Denis and Baldridge, 2007) the features for coreference resolution on pronouns and nonpronouns are usually different.


the training process usually takes much longer time than unsupervised or deterministic systems.


The upper bound, "goldnp" only considers gold NP-nodes to be candidate mentions.


Sentence distance between the potential antecedent and anaphor; c. Syntactic tag of them, whether they are leaf node or not in the parsing tree; d. Syntactic tag bi-grams of them, and whether their syntactic tags are identical; e. Named entity tags of them, bi-gram of these tags, and whether they are identical; f. Syntactic tag path to root of them, bi-gram of these paths, and whether they are identical; g. Whether they are predicates; h. Features of anaphor: Its head predicates and types, words, pos tags, the words and pos tags of the left/right 3 neighbor tokens, and bigrams; i.

we consider mentions that correspond to a word or an internal node in a syntactic tree and ignore the rest mentions, as we think a mention should be a valid meaningful unit of a sentence.


We used the English version and constructed 6 source-target ordered domain pairs from the original 3 domains: B2D, D2B, B2M, M2B, D2M, M2D.

between the instance (x s,r ) from the source domain and the instance (x t,r ) from the target domain, which one should we select for further consideration?


In the remainder of this section we describe the mathematical foundations for stopping methods based on stabilizing predictions.

the different metrics differ in how they compute A e .


In addition, the information presented in this pa-per is useful for works that consider switching between different active learning strategies and operating regions such as (Baram et al., 2004;Dönmez et al., 2007;Roth and Small, 2008).

the following theorem generalizes theorem 3.3 to cases when M P ositiveConjunction has precision p in (0, 1).


One way to adapt it for document significance is to alter the numerator such that only the span-constrained bigram occurrences in -significant documents are considered in computing f (x, y).

consider a document with f occurrences of a word-pair of which span of f s occurrences is atmost s, the given span threshold.


cPMId addresses this weakness by explicitly bounding the probability of observing a given deviation between f (x, y) and its expected value f (x) * f (y)/W .


Intuitively, the CRF-based supervised learning approach should yield high segmentation accuracy when there are large amounts of annotated training data available.

(2009) and the semisupervised Morfessor algorithm (Kohonen et al., 2010).


Since q retains its decomposition, the normalization can be efficiently computed using forward-backward.

to do so, we build on the posterior regularization (PR) framework of Ganchev et al.


Compared with Semi-CRF, Semi-Boost showed lower memory consumption.

in the first iteration, Semi-PER is trained with the initial weights of samples.


However, the training time increases linearly by the number of boosting iteration.

to apply boosting to the semi-Markov perceptron, the following methods are proposed; 1) Use the weights of training samples decided by AdaBoost as the learning ratios of the semi-Markov perceptron, and 2) training on AdaBoost with the loss between the correct output of a training sample and the incorrect output that has the highest score.


For example, lexical resources such as lists of names, locations, abbreviations and stop words were used (Daumé III and Marcu, 2005), and a full parser output was used in (Zhang et al., 2001).

semi-PER and semi-Boost only consider features that appeared in correct ones and incorrectly recognized ones.


Let Φ a , Ψ a , Ξ a , Υ a be the projection matrices from proposition 5.1.

input: samples of (X, A1, A2, F1, F2, P, R, D, B); feature functions φ, ψ, ξ, and υ; number of hidden states m Output: estimates Ĉ b|a ,Ĉ * |a ,ĉ 1 a ,ĉ a x of the operators used in algorithm 2 [Singular Value Decomposition] • For each label a ∈ [l], compute empirical estimates of and obtain their singular vectors via an SVD.


Phoneme recognition is often modeled with a fixed-structure HMM trained with EM, which makes it a natural application for spectral training.

projecting this transformation into the spectral algorithm for L-PCFGs is cumbersome and unintuitive.


Similarly, consideration of dependency arcs allows the compressed dependency tree z to be scored using a rich set of indicator features over dependency labels, part-of-speech tags and even lexical features as in Filippova and Strube (2008).

we observe a significant gain for the joint structured transduction system over the Clarke and Lapata (2008) approach for n-gram F 1 .


In our study feature vectors serve a complementary purpose, while the main focus is to study the virtue of structural representations for reranking.

different representations lead to different models described below.


(1992) to greedily maximize the likelihood of the data.

using the language of probabilistic graphical models, this means that the distribution of the semantic classes factorizes in the tree defined by π (See Fig.


Input to the model is a reference embedding matrix, i.e.

3 Though this is a principled way of handling new words in NLMs, the by-product word representations, i.e.


We also report baseline results (rows HSMN, C&W) using these initial embeddings alone, which interestingly reveals strengths and weaknesses of existing embeddings.

at any local decision (a dotted node), a new parent word vector (p) is constructed by combining a stem vector (x stem ) and an affix vector (x affix ) as follow: Here, W m ∈ R d×2d is a matrix of morphemic parameters while b m ∈ R d×1 is an intercept vector.


(4) To enhance precision, we reject a segmentation if it has either an affix or an unknown stem (not a word by itself) whose type count is below a predefined threshold 7 .

rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors.


Lastly, as English is still considered limited in terms of morphology, our model could potentially yield even better performance when applied to other morphologically complex languages such as Finnish or Turkish, which we leave for future work.

their models can only combine a stem with an affix and does not support recursive morpheme composition.


For example, Pattern 17 says that (1) this is a DF pattern; (2) the relation type is dobj; (3) the frame name of the head is EF; (4) the frame name of the dependent is Weapon; (5) the head is not negated; and (6) we don't care about the sentiment of the head.

convoluted posts appearing later in long post sequences.


The question, then, is: how can we exploit information in SameStancePosts(p) to improve the classification of p?

killing babies is wrong so stop doing it.


Our goal in this paper is to improve the state of the art in stance classification of online debates, focusing in particular on ideological debates.

to answer this question, we applied both classifiers to the de-   velopment set for each domain and obtained the results in table 3.


Row 1 shows the results of the Anand et al.

we sum up the confidence values 2 assigned to the set of test posts written by the same author for the same debate domain.


Since the proportion of correct answers is small (around 10%), the evaluation measures precision and recall over true answers predicted by each system.

this baseline would perform well if our negative facts were noticeably disconnected from our database.


For metrics which require a probability distribution, we pass the vectors through a sigmoid to obtain if we know both that (Greeks, are, mortal) and (men, are, mortal).

the computational cost becomes unreasonable with a large database, and only a small portion of the database is likely to be relevant.


We also explore our system's performance on subsets of the ACE dataset, and on the OntoNotes dataset.

since alignment itself is a challenging task and is often noisy, existing systems either align conservatively, resulting in very few links, or combine the attributes of multiple candidates, leading to a conflation of entities.


To run inside-outside recursion, the EM algorithm employs the parameters θ based on maximum likelihood estimation; the VB algorithm employs the mean parametersθ; and our CVB algorithm employs the parameters θ CVB computed from the expected counts of all other sentences.

hence, the independence assumption in (2) and Figure 1(d) seems too strong, leading to inaccurate local maximums, although it allows for efficient and deterministic updates in EM and VB.


Variational Bayesian inference (VB) (Kurihara and Sato, 2006) for PCFGs extends EM and places no constraints when updating parameters in the M step.

there remains a challenge for more efficient, but also accurate and deterministic inference algorithms for PCFGs.


In order to encourage sparse grammars and avoid overfitting, recent research for training PCFGs has drifted away from MLE in favor of Bayesian inference algorithms that make either deterministic or stochastic approximations (Kurihara and Sato, 2006;Johnson et al., 2006;Johnson et al., 2007).

mCmC algorithms improves the performance over Em and VB at the cost of much more training time.


They show the good behavior of the system, mainly when speech input is considered.

3), the best results are achieved when just one or two hypotheses are provided by the translator.


To train these LMs, the training sentences of the corpus in the target language must be segmented and labeled in terms of the concepts of the task.

in order to learn a domain-specific translation model, a parallel corpus is automatically generated from the training set by using web translators.


One of the advantages of statistical models is the capability of representing the variability of lexical realizations of concepts (meanings).

in the case of supervised learning, it is necessary to define a set of concepts that represent the semantic domain of the task and to associate these concepts to the corresponding sequences of words in the sentences.


To perform this maximization, we propose a decoupled architecture, which sequentially applies all the knowledge sources.

they are usually plain models, that is, they can not represent a hierarchical semantic dependency, although there are some works in this area (He and Young, 2003).


For multi we can see the impact of the pivot languages individually and in groups.

ccB is used with 10 pivot languages (English is included as a pivot) to generate paraphrase candidates.


Similar to (Bannard and Callison-Burch, 2005), we weight our paraphrase probabilities using an LM to adjust it to the context of the input sentence.

the approach does not solve the more complex issue of polysemous paraphrases: words with the same category but different meanings, such as the noun bank as financial institution and land alongside a river/lake.


For the other methods, we calibrated the thresholds to get the same number of extractions.

although topic models can group related e and f in the same topic cluster, the extraction of a high-precision dictionary requires additional effort.


Words w l are drawn from languagespecific distributions p(w l |z l , ϕ l ), where language l indexes English e or Foreign f .

there may be enough statistics train IBM-1 on documents.


If not otherwise stated, the configurations in the following sections apply the BNP1 acquisition pattern, the BBNP heuristic, and the LRT-ranking with a minimum score of 3.84.

while the original measure (Damerau, 1993) is only defined for single word terms, Kozakov et al.


We design a simple likelihood ratio test to operationalize this assumption.

all improvements are statistically significant 10 .


Table 1 shows examples of the five error types in our shared task.

in order to allow the participating teams to raise their disagreement with the original gold-standard annotations provided by the annotator, and not understate the performance of the teams, we allow the teams to submit their proposed alternative answers.


We observed that adding the LM feature improves precision but results in lower F1, so we chose the AP classifier without the LM feature for our final system.

incorrect determiner deletion: This had shown that the engineering design process is essential in solving problems and it ensures that the problem is thoroughly looked into and ensure that the engineers are generating ideas that target the main problem, *the/∅ depletion and harmful fuel.


Some countries are having difficulties in managing a place to live for their *citizen/citizens as they tend to get overpopulated."

we found that training an AP model on the ESL training data with more sophisticated features is not as effective as training on a native English dataset of larger size.


Grammatical error correction in writing of English as a second language (L2) or foreign language (EFL) is an important issue, for there are 375 million L2 speakers and 750 million EFL speakers around the world (Graddol, 2006).

this module keep the original word because of the 3rd person singular present verb, 'has'.


Each Noun Phrase (NP) in the test sentence will be pre-corrected as correction candidates.

this paper describes the NLP 2 Ct Grammatical Error Detection and Correction system for the CoNLL 2013 shared task, with a focus on the errors of article or determiner (ArtOrDet), noun number (Nn), preposition (Prep), verb form (Vform) and subject-verb agreement (SVA).


The normalized language model score is defined as in which s is the corrected sentence and |s| is the sentence length in tokens .

sVA is more complicated and it is more effective to determine the mistakes by using the linguistic and grammatical rules.


The NUCLE contains more than one million words (1,400 essays) and has been annotated with error-tags and correction-labels.

the hybrid method of our system has filtered some wrong suggestion candidates that improve the precision.


Clauses that are still longer than 30 words are not decoded.

we are especially interested in how frequently the correct sentence is among the hypothesis sentences -this is called the hypothesis coverage.


These approaches may not produce [ These ill-formed sentences are particularly harmful for SMT systems which, unlike classifiers, work at a global rather than local level.

this may be partly a result of the larger amount of training data in our final system and/or greater grammatical or thematic similarity between the test and training sets.


an indefinite to a definite one or vice versa) or simply delete it.

we introduce here a participating system of the CoNLL-2013 Shared Task "Grammatical Error Correction".


A new token can only be added before or after an existing token.

previous approaches Rozovskaya et al., 2012;Kochmar et al., 2012) manually decompose each error types to more detailed ones.


Note that they are not ngrams of POS tags, as could be assumed from the name; the name refers to the manner in which they are constructed.

the morphological analysis simply consists of searching for a word form in the list, while the morphological generation involves searching the list of lemmas and then finding the word form with the necessary POS tag, i.e., for the generation, the input consists in the lemma and the POS tag.


The classifiers take word tokens as input; no deep grammatical information was supplied to them.

from the predictions of the five classifiers the following eight error correctors are derived.


Even though the training data supplied for the task contained syntax trees, they were not used in creating our system.

here is an example of a determiner which is corrected: .


However, the main difference between our system and previous ones is that we do not select our confusion sets in advance of statistical modelling.

they could use corpora better matched to the data set, and they could have larger vocabulary sizes.


In choosing our confusion sets, we wanted to be flexible and extensible.

a sentence may be parsable in a language, even if it is nonsensical.


Table 4 shows that the feature name addition does not improve the precision in the case of article correction, and the set of 18 features achieves the best performance for article correction.

the same word can be extracted again as a different kind of features.


For instance, LAPOS is significantly (p < 0.05) better than STANFORD on sentences shorter than 16 words in EMAILS, but not on sentences shorter than 14 words.

in this paper, we follow Berg-Kirkpatrick et al.


2010, which consists of product images and captions scraped from the shopping website Like.com.

gIST Nearest-Neighbor Extraction: Classic ballet flats with decorative canvas strap and patent leather covered buckle.


We then use an EGY language model that is trained on CODA-compliant text.

fourth, CODA is intended as a unified framework for writing all dialects.


It is a binary feature signaling the presence or absence of words with the same length of the clue answer (only used for snippet reranking).

we only considered the text from Bing snippets.


This is an advanced QA pipeline based on deep linguistic processing and semantic resources.

substructures are composed by atomic building blocks corresponding to nodes along with all of their direct children.


The results for BL, CJ08, EE verb and EE are shown in Figure 3.

we evaluate our approach in two different set-ups.


The selected model used the dimensionality of 10 for event and word embeddings.

our embedding method would be expected to capture relevant features of the verb frames, namely, the transitive use for the predicate disembark and the effect of the particle away, and these features will then be used by the ranking component to make the correct prediction.


Both CW and HLBL vectors were trained using 37M words from RCV1.

is supported by WeedsRec outperforming Weed-sPrec, although the opposite was intended by their design.


Then the best hyperparameter values were trained on the combination of training and development data and applied on the test set, to obtain the final results.

the skip-gram model then computes a probability of a context word given a target word as the product of the probabilities, given the target word's embeddings, of all decisions on a path from the root to the leaf corresponding to the context word.


First we group the tokens into phrases, assigning to each token a single phrase greedily.

while lexicons have proven useful in various NLP tasks, a small amount of noise in a lexicon can severely impair the its usefulness as a feature in log-linear models.


We use logistic regression, with features φ p ij and parameter w p , as a probabilistic model of predicate entailment (and so for arguments with φ a ij and w a ): where σ is the sigmoid σ (z) = 1 1+e −z .

we create a large dataset of gold-standard proposition entailment graphs, and provide a novel algorithm for automatically constructing them.


The current version of the English Wikipedia contains information about 4.2 million entities.

moreover, the main goal of CoTS is to predict temporal ordering of relations rather than to scope temporally individual facts.


On November 18, 2006, Holmes and Cruise were married in Bracciano, Italy, in a Scientology ceremony attended by many Hollywood stars.

all these efforts focus on temporal ordering of either events or states of the world and do not extract timestamps for events.


Compared to the annotation projection approach of Hwa et al.

several extensions have been proposed to make the approach more robust.


It makes use of the decoding mechanism of the phrase-based model which jumps over the source words and hence can hold discontinuous phrases naturally.

translation performance was evaluated using BLEU (Papineni et al., 2002) with case-insensitive n ≤ 4grams.


Of the 16, 269 unique unigrams in the source side of our Spanish MT tuning and test sets, 73% are OOV with respect to our training corpus.

the views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government.


At k " 1, precision and recall are about 27% for Spanish and about 25% for Hindi.

our oracle experiments showed potential for composing phrase translations for both language pairs, and, indeed, in our experiments using hallucinated phrase translations we saw significant translation quality gains for both.


Prior work has reduced the size of SMT phrase tables in order to improve efficiency without the loss of translation quality (He et al., 2009;Johnson et al., 2007;Zens et al., 2012).

it is useful for the translation model to know that, in the context of evitarlos, para should translate as to and not for.


Furthermore, they use 'discounting' methods to improve the estimates of conditional probabilities when learning data is sparse.

first, though our model still figure 1: Mean precision scores as a function of window order, obtained against the Wordnet-based gold standard, on both the verb and noun test-sets with both the synonyms and semantic neighbor detection tasks.


We further conjecture that the reason the word embedding baselines did not do as well as our model on verb similarity might be due to their particular choice of joint-context formulation, which is not sensitive to word order.

they showed good results on detecting synonyms in the 80 multiple-choice questions tOEFL test.


In the 'top-100 scores' results, for each method we converted to zero the scores that it assigned to word pairs, where neither of the words is in the top-100 most similar words of the other.

we believe that our approach is well suited to address context sensitive similarity with proper enhancements, as it considers joint-contexts that can more accurately disambiguate the meaning of target words.


Conversely, if a pair of phones is not forming any minimal pair, it is classified as non-allophonic (phonemic).

the semantic information here would basically enable us to avoid false alarms generated by potential true minimal pairs like the above-mentioned example of ( [kanaX] and [kanal]).


For example, in a much simplified error correction task concerning only the correction of prepositions, Tetreault and Chodorow (2008) showed an actual difference of 10% precision and 5% recall between two annotators.

the probability of a learner n-gram is compared with the probability of a candidate corrected ngram, and if the difference is greater than some threshold, an error was perceived to have been detected and a higher scoring replacement n-gram could be suggested.


Such decisions will lower the total number of unnecessary edits, thus improving overall precision.


The test set in each of the four training/testing runs is again divided into two halves.

although results (Table 6) with error selection increase for 4×2-CV, the NUCLE+CCLM+L8a seems to generalize poorly to new data, there is a significant drop for the external test set.


Lang-8 is scraped for additional entries and we manage to nearly double the size of the corpus to 3,733,116 sentences with 51,259,679 tokens on the source side.

moses has not been designed as a grammar correction system, the standard features and optimization methods are geared towards translation performance measured by the metrics used in the SmT field.


To address this problem, the Illinois-Columbia system makes use of global inference via an Integer Linear Programming formulation .

it should be noted that the proportion of erroneous instances is several times higher in the development data than in the training data for all of the error categories.


Note that although for the development set we make use of last year's test set, these results are not comparable to the performance results reported in last year's competition that used the F1 measure.

the modules targeting verb agreement and verb form mistakes draw on the linguisticallymotivated approach to correcting verb errors proposed in Rozovskaya et.


In our experiments we noticed frequent cases where the LM does not provide sufficient support to distinguish between true grammatical errors and simply unusual but grammatically correct expressions.

this 4 billion n-gram corpus should provide high-quality perplexity estimations.


Furthermore, they do not implement any smoothing method for the 1T corpus, defaulting to the +1 "stupid smoothing" as they themselves named it, relying on the fact that smoothing is less relevant with a very large corpus.

the system they propose is written in Java, a language that is objectoriented, and which, for any object, introduces an additional overhead.


In general, GEC may partly assist in solving natural language processing (NLP) tasks like Machine Translation, Natural Language Generation etc.

the weight parameters (λ i ) define the relative weights given to each feature.


Other Correction Candidate: for corrections other than noun number, candidates are selected from the GE-tagged corpus.

detailed description of the shared task set up, data set, and evaluation about the CoNLL-2014 Shared Task is explained in (Ng et al., 2014)  The Google Web-1T corpus contains 10 12 words of running text and the counts for all 10 9 five-word sequences that appear > 40 times (Brants & Franz, 2006).


The task of CoNLL-2014 is grammatical error correction which consists of detecting and correcting the grammatical errors in English essays written by non-native speakers (Ng et al., 2014).

a single maximum entropy model is used for the classification implementation incorporated with an improved feature selection algorithm.


In addition, dividing the error detection and correction into two steps alleviates the application of machine learning classifiers.

suffix: suffix, one of the important factors, helps to capture the grammatical agreements between predicates and arguments within a sentence.


We tune our thresholds for making corrections on development data 7 , and found that deleting a determiner should have a lower threshold while inserting one should have a higher one, so we set different thresholds accordingly.

the tuple, (join, party), which deletes the preposition, is also taken into consideration.


If a word is not in the dictionary but one of its splitting results has a high frequency, we will split the word into two words.

it's easier to make meaningful comparison with previous works.


Denote by E X ( (x)) the expected loss on unseen data when using w T,K where (x) = U (x, y * ) − U (x, y ), y * = arg max y U (x, y) and y = arg max y max h w T,K φ(x, y, h).

we would like to evaluate predictions that use the final weight vector w T,K by comparing the generalization error with the empirical error stated in Theorem 3.


The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA or the U.S. Government.

there are significant gaps between the performance on gold mentions and the performance on the real problem, when mentions are predicted from raw text via an imperfect Mention Detection (MD) module.


Adding context parameters never hurts, and in most cases, using priors based on intrinsic, cross-lingual aspects of the CCG formalism to bias those parameters toward connectivity provides further gains.

learning parsers from incomplete or indirect supervision is an important component of moving NlP research toward new domains and languages.


Most of ne words have another nonnamed entity meaning and in most cases these other meanings tend to be MSA.

it provides a binary flag "isNE" for each word in the input sentence.


The second dataset is the 20 Newsgroups dataset (Lang, 1995) which is a text collection of approximately 20, 000 documents evenly partitioned across 20 newsgroups.

novel predictive analytics techniques on standard datasets (or limited client data) did not generalize across different domains ( new products & services) and has limited applicability.


The philosophy of our algorithm is gradual transfer of knowledge from the source to the target domain while being cognizant of similarity between two domains.

the proposed algorithm iteratively learns discriminative target specific features from such perplexing data and translates it to an improvement of at least 6.4% and 3.5% over the baseline and the SCL respec- Figure 7: Results comparing the accuracy of the proposed approach with existing techniques for cross domain categorization on the real world dataset.


the proposed algorithm differs in the following ways: 1) an unsupervised approach that transforms unlabeled data into pseudo labeled data unlike OTL which is supervised, and 2) incorporates similarity in the adaptation setting for gradual transfer.


This could potentially allow us to manipulate the verbosity of the translation hypotheses generated at test time by changing some characteristics of the tuning dataset.

for Spanish-English, the story is different: here the English sentences tend to be shorter than the Spanish ones, and the verbosity decreases as the sentence length increases.


We observed that MERT learns the verbosity of the tuning dataset very well, but this can be a disadvantage because we do not know the verbosity of unseen test sentences.

below, we present an example that makes this evident.


Pathological verbosity was reported when using MERT on recall-oriented metrics such as ME-TEOR (Lavie and Denkowski, 2009;Denkowski and Lavie, 2011), as well as large variance with MIRA (Simianer et al., 2012).

to this end, we have studied the impact that source-side length and verbosity of tuning sets have on the performance of the translation system when tuning the system with different optimizers such as MERt and PRO.


The correlation is significant (p < 0.001) when we take into account the contribution of the tuning set verbosity in the model.

when several tuning sets are available, we recommend choosing the one closest in length to the test set and using MERT.


Next, lines 6 and 7 show the results when applying our smoothing fix for sentence-level BLEU+1 (Nakov et al., 2012), which prevents translations from becoming too short; we can see that long yields very comparable results.

in our study, we use the source-side sentence length as a data selection criterion; still, we also report results for selection based on verbosity.


For example, for a word "want" with POS tag "VERB", its feature value for "VERB → want, RIGHT" is likely to be very small such as zero, while its feature value for "want→ NOUN, LEFT" is likely to be large.

here we used this method in our setting with only one source domain.


We further extend our analysis by performing pairwise post-hoc Mann-Whitney (MW) tests (Mann and Whitney, 1947) in order to determine the number of language pairs that significantly differ with respect to their native speakers' error fractions in ESL.

we perform several preprocessing steps in order to select the features that will be used in this study.


Coupling grammatical errors with typological information also provides meaningful explanations to some of the linguistic factors that drive the observed error rates.

far, we presupposed the availability of substantial typological information for our target languages in order to predict their ESL error distributions.


Always choosing English as the source language performs worst.

it appears that the best choice of an appropriate source language is not predictable from language family information.


In summary, existing work generally starts with a delexicalized parser, and uses parallel data typological information to improve it.

in summary, existing work generally starts with a delexicalized parser, and uses parallel data typological information to improve it.


This confers the benefit of generalising over the vocabulary to alleviate issues of data sparsity, through learning representations encoding lexical relations such as synonymy.

we want to improve the delexicalized parser, but without using parallel data or any explicit linguistic resources.


The UAS/LAS column show the average UAS/LAS for all target languages, excluding the source language.

for many languages, annotated treebanks are not available, and are very costly to create (Böhmová et al., 2001).


Most Q&A community sites advise users to search the forum for an answer before posting a new question.

the results presented in table 4 show that training on in-domain data is more beneficial for the network, even though the corpus used to create word embeddings is much smaller.


More generally, any discriminative model that is trained to predict individual actions has this problem.

discriminative learning further improves this aggressive pruning, because it can optimize for the discrete choice of whether to prune or not (Huang et al., 2012;Zhang and Clark, 2011).


In cases in which no such parallel resources are available containing pertinent annotations, models can be transfered after training.

it increases from 465 to 827 while the gold number is 676.


In this paper, we perform annotation projection, which is one of the two main categories for cross-language model induction (next to direct model transfer (Agić et al., 2014)).

in the following, we explain our procedure for inducing a model for a target language for which no annotations are available.


in short, MORFETTE treats the task as a string-to-string transduction problem, whereas we view it as a labeled segmentation problem.


But only 1824 words find no embedding from any channel!

this kind of initialization is able to make use of information about partially known words, without having to employ full random initialization or removal of unknown words.


The authors make use of the observation that morphologically related adjectives exist for PT-verbs, unlike for AG-and SP-verbs.

on MPQA, only the new induction approach and the lexicons significantly improve CK.


For instance, in 17and 18, the opinion holder is assigned to the same frame element EXPERIENCER.


rationales contain the information relevant to the establishment of a link.


M SP is computed using the original set of SPs, excluding the two antonym patterns, while M AP is computed using the two antonym patterns only.

to the best of our knowledge, they have not been applied to vector space word representation.


These features include lexical bag-of-words features and features based on syntactic parse trees.

while simply adding word embeddings trained using window-based contexts as additional features to existing systems has proven valuable (Turian et al., 2010), more recent studies have focused on how to tune and enhance word embeddings for specific tasks (Bansal et al., 2014;Boros et al., 2014;Chen et al., 2014;Guo et al., 2014; Nguyen and Grishman, 2014) and we continue this line of research for the task of relation classification.


(2014), which used sentiment labels on tweets to adapt word embeddings for a sentiment analysis tasks.

our method predicts each target word w in i ∈ w in using three kinds of information: n, words around w in i in w in , and words in w bef and w af t .


There are several lessons to learn from this paper.

the research question underlying this paper is whether we can accurately learn to take greedy parsing decisions for rich but complex structures such as spinal trees.


Table 4: Correlations between human judgments and metrics (micro avg).

in addition, we compute correlations between metrics to assess their similarity.


Motivated by the fact that people prefer the parse that gets the overall structure right ( §5), we experimented with weighting edges proportionally to their log-distance to root.

for the human judgments, we asked professional linguists with dependency annotation experience to judge which of two parsers produced the better parse.


Further, it is observed that IADWPT performs well at feature selection without losing discriminative information, even when the dimensionality of feature space is reduced to as far as 1/40th of original feature space and steadily maintains the accuracy as dimensionality is reduced, which makes it a suitable technique for aggressive dimensionality reduction.

we name it as improvised adaptive discriminant wavelet packet transform (IADWPT).


Diab (2004) proposed an unsupervised bootstrapping method to automatically generate a senseannotated dataset.

these datasets either include few samples per word sense or only cover a small set of polysemous words.


In the 1980s our focus was in trying to find an alternative to the word "semantics", which in practice was becoming "look it up in the dictionary" or "follow a link in a semantic net".

we moved away from the name "Machine Learning of Natural Language" which was the title of the book based on my own PhD thesis (Powers and Turk, 1989) while the AAAI event I organized added "and Ontology" (Powers, 1991a).


We train our models with stochastic gradient descent, and at each gradient step we estimate the reconstruction cost using a  Figure 1: The core structure of our variational autoencoder language model.

this gives evidence for our intuition that the vae learns representations that are smooth and "fill up" the space.


indistinguishable samples), and the actual accuracy attained.

table 12: Selected homotopies between pairs of random points in the latent vae space.


Since our decoder model p(x| z) is a sophisticated rnnlm, simply sampling from the directed 100% word keep 75% word keep " no , " he said .

by breaking the model structure down into a series of next-step predictions, the rnnlm does not expose an interpretable representation of global features like topic or of high-level syntactic properties.


Note that the all resources created within this work are publicly available.

the underlying idea is that instead of using a single view on the data (as done in prior work), multiple views result in better temporal classification accuracy.


In order to produce comparative results with the best-performing system at TempEval-3, namely UTTime (Laokulrat et al., 2013) for the above task, we follow the guidelines and use the same datasets provided by the organizers (UzZaman et al., 2013).

whereas most prior studies have focused on temporal expressions and events, there has been a lack of work looking at the temporal orientation of word senses.


FUTURE_REF) are considered as the temporal class for that particular synset.

discovering the temporal orientation of words is a challenging issue that may benefit many text applications.


To address the lexical sparity issue, one direction is to enrich text representations by extracting features and relations from Wikipedia (Banerjee et al., 2007) or an ontology (Fodeh et al., 2011).

we only need to minimize the first term by assigning each text to its nearest cluster centroid, which is identical to the E-step in the k-means algorithm.


In the E-step, the algorithm minimizes J unsup with respect to {r nk } by keeping {µ k } fixed.

the labeled data are quite useful for the clustering process.


(2014), Table 4 shows that TransE in fact obtains a very competitive accuracy.

note that the incorrect triples in the validation and test sets are not used for evaluating the entity prediction task nor the relation prediction task.


This is called as the "Raw" setting protocol.

for the entity prediction task, we selected δ = 10 and λ = 0.005 for WN11, δ = 5 and λ = 0.01 for FB13, and δ = 5 and λ = 0.005 for NELL186.


Having a comprehensive semantic framework which can represent the underlying meaning of comparison structures is the first step toward enabling such an inference.

although ellipsis has been studied in great depth in linguistics, there only have been a few computational studies of el-lipsis, most of which have focused on Verb Phrase Ellipsis (VPE) (Nielsen, 2004;Schiehlen, 2002;Hardt, 1997) such as Larry is not telling the truth, neither is Jim ∆.


For example, going to a restaurant involves entering the restaurant, getting seated, making an order and so on.

it would fail to take into account the semantic similarity between words prepared and cook, dog and cat.


Adding the reference to the temple decreases confidence again to 0.79.

both systems will likely benefit from our improved verb prediction presented here.


Our aim is to investigate the extent to which CF on SOEs plays a part in overcoming such errors during language acquisition.

most instances of CF (30.8%) occur as a response to subject omission errors (SOEs); an example can be found in excerpt (2a), Section 3.2.


Compared to the training error reported above we naturally observe a drop in performance when predicting PoS for this unseen data, but the classifier still appears quite robust, yielding an F-score of 0.91.

in this case the distributional model highlights the already known similarity between two word classes.


Educators often use cloze tasks to evaluate learner vocabulary (though these generally use L2 context).

7 We compute three different features for each language model: a raw LM score, a sentencelength-normalized LM score, and the difference between the LM score with the correct answer in the sentence and the LM score with the candidate in its place.


Strictly, the improvement in the compression cost is adequate proof that the presented model outperforms the baselines.

the last 15 years have seen a surge in computational modeling of language relationships, change and evolution.


Finally, for any pair of languages L 1 and L 2 , the model in general produces different distances for (L 1 , L 2 ) vs. (L 2 , L 1 ), depending on which language is the source and which is the target (since some languages preserve more information than others).

the code will assign a cost (code-length) to every possible alignment of the data.


Recall increases by 11.8% when sequence labeling techniques are used instead of classification.

we observe that for sarcasm detection of our dataset, in case of different feature configurations, sequence labeling performs better than classification.


Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels.

the difference in the F-scores is not statistically significant (p = 0.21) for dataset 2 for the best and second best feature combinations.


(2015) proposed an efficient joint model for CCG syntax and SRL, which performs better than a pipelined model.

query provides a summary of the stack in a single fixed-length vector.


Figure 1: Interactive translation of a Spanish sentence into English.

we use hypergraphs to represent such search space.


The posterior distribution is obtained by applying a change of variables: where µ * and σ * are the mean and standard deviation of the latent (warped) response variable and f and f are the warping function and its derivative.

most kernel methods tie all lengthscale to a single value, resulting in an isotropic kernel.


A usual way to deal with this problem is model the logarithm of the response variables, since this transformation maps strictly positive values to the real line.

different kernels can be more appropriate depending on the language pair, but more experiments are needed to verify this, which we leave for future work.


Most kernel methods tie all lengthscale to a single value, resulting in an isotropic kernel.

• We intrinsically evaluate our proposed models in terms of their posterior distributions on training and test data in Section 3.


(2006), which defined new evaluation metrics that take into account probability distributions over predictions.

there is no reason to believe this is the best possible mapping: a better idea would be to learn it from the data.


In the future, we would like to experiment with combining our method with other techniques for multilingual NER (Section 2), including parallel projection and the automatic generation of training data from Wikipedia.

it makes sense that high-resource languages are better training languages because 1) there are more annotated training instances, 2) larger Wikipedia creates denser wikifier features, therefore providing better estimation of the weights to these features.


This approach does not rely on external semantic features, but rather employs a combination of local and sentential features to describe individual event mentions, and combines these intermediate event representations with standard pairwise features for the coreference decision.

even when taking into account the caveats mentioned above, it seems justified to assess that our model performs in general on-par with their stateof-the-art system.


Further, coherence based on unambiguous entity mentions and our two-step approach significantly improved performance on the CoNLL dataset.


For example, string similarity or edit distance between a mention surface and an entity's canonical form (Cucerzan, 2011;Cassidy et al., 2011), and the prior probability of a mention surface being some entity, etc.

aQUaINT is originally collected by (Milne and Witten, 2008) and mimics the structure of Wikipedia.


This implementation has the advantage of simplicity as it requires minimal changes to the training and deployment code, but we note that a more complex implementation utilizing sparse matrices and sparse matrix multiplication could potentially yield speed improvements.

this may seem surprising, as we might not expect a sparse model to significantly out-perform a model with five times as many parameters.


Suppose we wish to prune x% of the total parameters in the model.

we continue to train the remaining weights, but maintain the sparse structure introduced by pruning.


The Penn Discourse Treebank (PDTB) is the largest available discourse-annotated corpus in English (Prasad et al., 2008).

following the UID principle, linguistic choices made by speakers are predicted more accurately by incorporating an information density predictor on top of other constraints.


For example, if the DC 'but' is omitted in Example (1), readers may have problems in inferring the Concession sense.

how a speaker makes this choice is a question that is not well understood.


A confident prediction means the parser will assign a high probability to the one output sense.

our study shows that a speaker organizes the discourse structure by balancing the pro (informativeness) and con (production cost and redundancy) of using an explicit marker, although the option is a subtle preference in the absence of other grammatical constraints.


The first one is that convolution captures the important domain information of the words in the window, and the max-pooling operation combines the vectors which, as a result, focuses on the most important "features" in the sentence.

data selection techniques select monolingual or bilingual data that are similar to the indomain seed data based on some criteria, which are incorporated into the training data.


The combination of all y k gives enough information to construct y.

grammatical relations (gRs) represent functional relationships between language units in a sentence.


Furthermore, the graph decomposition procedure is utilized to generate training data for building sub-models.

furthermore, the graph decomposition procedure is utilized to generate training data for building sub-models.


Figure 1 shows that verbs having the same radical are relatively similar to each other compared to verbs which belong to different radical groups.

the incorporation of all the 17 features does improve the classification model by 1.70% in F-score.


(2016) point out that the challenge arises from the highly context-dependent property of homonymies since the relations of senses are not unsystematic.

a set of syntactic conditions core to the event structure of a verb can define where its literal senses tend to occur.


With this adjustments, the algorithm needs only to evaluate candidate merges with the newly constructed entity and therefore each iteration requires a linear number of scoring runs, leading to O(n 2 ) runs overall.

there are several important differences.


On our test data, TUPLEINF(T+T') achieves 46.17% P@1 (line 5).

in addition to previous work, we compare our model against two strong iR baselines: • iR Baseline: For this baseline, we rank answer candidates by the maximum tf.idf document retrieval score using an unboosted query of question and answer terms (see Section 4.1 for retrieval details).


(2008), p.163), where we assigned Good justifications a gain of 3.0, Half a gain of 2.0, Topical a gain of 1.0, and Off-Topic a gain of 0.0.

the lack of labeled data for answer justifications makes learning this difficult and expensive.


We make the following contributions: (A) We introduce the notion of question term essentiality and release a new dataset of 2,223 crowd-sourced essential term annotated questions (total 19K annotated terms) that capture this concept.

the questions were annotated by 5 crowd workers, 4 and resulted in 19,380 annotated terms.


Dotted lines represent the virtual link between words and associated senses in context.

their creation process does not allow the different meanings of a word to be automatically separated, as it conflates them into a single vector.


For evaluation purposes, we use the CBOW model of word2vec with standard hyperparameters: the dimensionality of the vectors is set to 300 and the window size to 8, and hierarchical softmax is used for normalization.

manually annotating large amounts of data is extremely expensive and therefore impractical in normal settings.


Note that this hyperplane is completely determined by the vectorp c and the choice of λ.

note that we write [x] + for max(0, x) and φ denotes the feature map of the considered kernel function.


In particular, for each word w j , we first determine a set of words w whose vector p w is inside these ellipsoids.

in this section we evaluate both the vector and region representations produced by our model.


We used the Apache sentence segmentation tool 2 to detect sentence boundaries.

for each word w j , we first determine a set of words w whose vector p w is inside these ellipsoids.


To learn the word embedding, we use the following objective function, which requires that for each context word w j there is a sequence of parallel hyperplanes that separate the representations of the words in W j i−1 from the representations of the words in j for each j.

we make the assumption that the higher PPMI(w, w j ) the stronger w is related to w j , but we do not otherwise assume that the numerical value of PPMI(w, w j ) is relevant.


Accuracy scores in Table 2 suggest that, even with small training data SVD can produce good vectors for the paradigmatic task.

this quick observation is consistent with previous findings regarding the superior performance of count models on word similarity and categorization tasks when models were trained on small corpora and with their default post-processing setting (Asr et al., 2016;Sahlgren & Lenci, 2016).


When context words are sampled from the same vocabulary as that of target words, the final CM will have the same dimensionality as WM, thus it can also be used as a semantic representation of the words.

the objective function and the input to word2vec are formally equivalent to traditional DSMs; thus the models should behave alike in the limit.


This methodology was inspired by the prominent study of Elman (1990) and we mainly selected that to bring confound factors in natural languages under control while assessing the effect of model parameters on produced word vectors.

we define two distinct methods to evaluate performance of the DSMs in learning semantic similarity from our artificial language-the syntagmatic task and the paradigmatic task.


Distributed representation of words (or word embedding) has been demonstrated to be effective in many natural language processing (NLP) tasks (Bengio et al., 2003;Collobert and Weston, 2008;Turney and Pantel, 2010;Collobert et al., 2011;Mikolov et al., 2013b,d;Weston et al., 2015).

the learning and updating are referred by the dashed arrow lines.


For example, a word has a topic vector ψ ] is the topic distribution matrix for all words with K topics; −−→ e (w i ) is the standard basis vector with 1 at the i-th position in the vocabulary V .

moreover, the regularizer can be constructed from either unlabeled data (e.g., LDA trained from the base corpus) or manually crafted resources such as a lexicon.


Work on annotator rationales (Zaidan et al., 2007;Zaidan and Eisner, 2008) seeks to model why annotators labeled a document a certain way-in other words, what "caused" the document to have its label?

ive estimation, negative training examples are synthesized by perturbing positive instances.


whether or not the mention starts a co-reference chain.

we use surface forms to represent discourse markers because there is only a limited set.


y t−1 ) i and a score for the next character y t .

recently, neural network models achieved state-of-the-art results for both types of segmentation tasks.


Location Authors such as Kavila and Radhika (2015) only chose summary sentences from the Abstract, Introduction or Conclusion, thinking these more salient to summaries; and we show that certain sections within a paper are more relevant to summaries than others (see Section 5.1).

on CSPubSum Train (the feature of which section the example appeared in was removed to do this).


Such a tendency gives more pure clusters, favored by the CEAF e metric for the scenelevel.

for each mention m i , it performs binary classifications between m i and m j where j ∈ [1, i).


For the scene-level, this grouping is done by each scene such that there can be multiple mention clusters that refer to the same character within an episode (C Sce ).

it is prone to breaking up too many links, which leads to poor performance in the B 3 evaluation on the episode-level.


We consider a training scenario where we have labeled examples for English q n , but we only have unlabeled exam- for Arabic q n , with c n,k denoting the class label for the pair (q n , q n,k ).

yet, when we consider the full adversarial network, in any of the two directions, we get performance that is on par with the best, in all metrics.


We apply this method to an extension of the SemEval-2016 Task 3, subtask B benchmark dataset for question-question similarity reranking (Nakov et al., 2016b).

it is also an exploration of training with data in multiple languages all together.


This indicates that the strong similarity contribution coming from word vectors is successfully affected by the word-in-context vectors from the network.

word Similarity Table 5 shows the results of encoding the words in context using a more sophisticated approach: mapping the word to a representation learned with the Siamese Network that we optimize on the derived classification task presented in Section 6.1.


We found that neural embeddings produced by current methods do not provide a suitable contextual similarity.

in Question 2, the word level loses its prevalent association with quantities.


The dataset is organized in a training, development and test sets containing respectively 9,728, 1,654 and 3,813 tweets.

sPTK would benefit to use specific word similarity when matching subtrees corresponding to different constituency.


The final loss is computed as where C f c and C l2 are hyperparameters which are set to 0 unless otherwise noted.


Some of these cases are completely wrong predictions.

each batch contains 100 questions of which only some are factoid and list questions.


The recent success of deep learning based methods on open-domain QA datasets raises the question whether the capabilities of trained models are transferable to another domain via domain adaptation techniques.

to existing biomedical QA systems, our system does not rely on domain-specific ontologies, parsers or entity taggers, which are expensive to create.


at the other extreme, with 37 splits each.

the first uses phonemic data from 9 languages for clustering and evaluates clustering along distinctive feature lines.


This paper explores the simplest incarnation of the idea of similarity avoidance; namely, that two adjacent segments are preferably different in some way and that this difference reveals itself globally.

the Individual column shows the macro-averaged results on running all languages individually, and the Allcolumn shows the results of running all data at once.


An almost identical SVD-based approach was later applied to phonological data by Goldsmith and Xanthos (2009).

l, n, and r are always in this set, while s is nearly always present.


The performance metrics used are also F1 measure and accuracy.

they are preprocessed using the same steps as the data set for creating sentiment lexicon.


However, it has been noticed that the propositions roughly correspond to certain POS tags.

(1996) mention that elementary propositions are expressed using verbs, adjectives, adverbs and prepositions.


We used distant supervision to select the specific sentences in which each R(e, a) manifests.

hence, the last elements ofz start andz end indicate the model's confidence that the answer has no start or end, respectively.